Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
Volume 2, Issue 2
235
Acumen: International Journal of Multidisciplinary Research
ANALYSIS OF METHODS, MODELS AND ALGORITHMS FOR A
COLLABORATIVE ROBOTS GROUP DECENTRALIZED CONTROL
Nataliia Demska1, Vladyslav Yevsieiev1, Svitlana Maksymova1,
Ahmad Alkhalaileh2
1Department of Computer-Integrated Technologies, Automation and Robotics,
Kharkiv National University of Radio Electronics, Ukraine
2Senior Developer Electronic Health Solution, Amman, Jordan
Abstract
The article considers modern methods, models and algorithms for a collaborative
robots group decentralized control, conducts a comparative analysis, identifies the
main advantages and disadvantages. Particular attention is paid to the problems of
scalability, coordination and adaptability in dynamic environments. The need to
develop new approaches to increase the efficiency of such systems in the context of
modern challenges in robotics is outlined.
Keywords:
Decentralized Control, Collaborative Robot, Analysis, Models,
Methods, Algorithm, Comparative Analysis.
Introduction
A collaborative robots group decentralized control is one of the key topics in
modern robotics, which is gaining particular relevance in the context of the transition
to Industry 5.0 [1]-[7]. Unlike Industry 4.0, which is focused on the automation and
digitalization of production processes, Industry 5.0 focuses on the harmonious
coexistence of humans and technologies, ensuring sustainable development and
increasing the level of personalization of production [5]-[11].
In this context, groups of collaborative robots play a central role, as they are able
to provide flexibility, autonomy and effective interaction in dynamic environments.
Various methods and approaches can also be used here [12]-[34]. Research into
methods, models and algorithms for decentralized control is an important step in
solving many applied problems related to distributed computing, cooperative execution
of complex tasks and resource optimization in multi-robot systems.
Acumen:
International Journal of
Multidisciplinary Research
ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
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Acumen: International Journal of Multidisciplinary Research
Despite significant progress in this area, there are a number of challenges related
to ensuring the stability and consistency of robot actions, as well as the adaptation of
algorithms to unpredictable changes in the environment. Of particular interest are
issues of synchronization, interaction under conditions of limited information, as well
as modeling robot behavior in the context of swarm intelligence, game theory and
multi-agent systems [35]-[44].
The results of research in this area have not only theoretical but also practical
value, as they contribute to the creation of effective solutions for application in such
areas as production automation, logistics, agriculture, rescue operations and research
of hazardous environments.
In this regard, the analysis of existing methods, models and algorithms of
decentralized control is necessary to determine the current state of research, identify
their advantages and disadvantages and form prospects for further development that
meets the requirements of Industry 5.0.
Related works
The increasing implementation of the principles of the Industry 5.0 concept has
led to the emergence of new challenges. Among them, we note the emerging need to
control a group of robots. Many scientists have been working on solving this problem
Multi-robot driving is a difficult problem [45]. The article [45] discuss how
human-robot collaboration and dialogue provide an effective framework for achieving
this.
The study [46] proposes a unified group coordinated control scheme for
networked multi-robot systems having multiple targets. There is noted that inspired by
the group activities of natural swarms (e.g., a flock of birds, a colony of ants, etc.), a
fleet of mobile robots can be collaboratively put into work to accomplish complex real-
world tasks. So, this is swarm method.
Scientists in [47] identify three core aspects of “Multi-agent” human-robot
interaction systems that are useful for understanding how these systems differ from
dyadic systems and from one another. Especially they consider systems containing
more than two agents (i.e., having multiple humans and/or multiple robots). They
summarize key observations from the current literature, and identify challenges and
promising areas for future research in this domain.
The author in [48] notes that Mivar decision-making systems can control groups
of small robots and even an unmanned autonomous car in real time.
Acumen:
International Journal of
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Enthrakandi Narasimhan, G., & Bettyjane, J. in [49] two co-operating mobile
robots with a multilayer control system which utilizes Boolean logic to enable the
significance of a relative behaviour. They try to control the robots in uncontrolled
environment and also in multitasking environment.
Sathyan, A., & Ma, O. in [50] introduce an approach of collaborative control for
individual robots to collaboratively perform a common task, without the need for a
centralized controller to coordinate the group. They use multiple robots performing a
collaborative task to achieve a common goal.
Researchers in [51] propose a Decentralized Ability-Aware Adaptive Control to
implement multi-robot collaboration that is extremely challenging due to the different
kinematic and dynamics capabilities of the robots, the limited communication between
them, and the uncertainty of the system parameters.
So we see that the issues of robot group control are very diverse. Further in this
article we will consider methods, models and algorithms of decentralized robot group
control.
Classification of modern methods, models and algorithms for a collaborative
robots group decentralized control
A collaborative robots group decentralized control is a hot topic in robotics,
especially in the context of Industry 4.0 and Industry 5.0. The main goal of
decentralized systems is to ensure the operation of a group of robots without a single
control center, using local interaction and data exchange between robots. Let us classify
existing methods of a collaborative robots group decentralized control in the context
of Industry 5.0, which is presented in Figure 1.
Let us conduct a comparative analysis of methods for a collaborative robots
group decentralized control, identify their advantages and disadvantages, and present
the results in Table 1.
The presented methods (Fig. 1 and Table 1) of for a collaborative robots group
decentralized control have significant potential, but at the same time they face a number
of significant limitations that complicate their effective application. Distributed
algorithms, although they ensure the stability of the system and its scalability, are often
unable to provide a high level of coordination between robots in large groups, which
can lead to uncoordinated actions or conflicts in the performance of tasks.
Acumen:
International Journal of
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ISSN: 3060-4745
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Figure 1:
Classification of methods for a collaborative robots group decentralized
control
The lack of complete information due to the locality of decision-making also
limits the accuracy and efficiency of such systems. Swarm intelligence algorithms
demonstrate excellent adaptability to changes in the environment, but their tendency to
local extremes and the inability to always guarantee a globally optimal solution create
risks for solving complex tasks. In turn, algorithms based on game theory provide high
efficiency in resource allocation, but their computational complexity and vulnerability
to unfair actions or failures of individual system components can significantly reduce
the reliability of operation.
Table 1:
Comparative analysis of methods for a collaborative robots group
decentralized control
Method
Description
Advantages
Disadvantages
Distributed
algorithms
In these algorithms,
each robot makes
decisions based on
local information
(obtained
from
sensors,
neighboring robots,
or the environment)
Lack of
dependence on a
central node,
which makes the
system more
resilient to failures.
Scalability: adding
new robots does
not
require
significant changes
to the system.
High complexity of
coordinating
actions in large
groups.
Limited accuracy
due to insufficient
information.
Decentralized control methods
Distributed algorithms
Swarm [46] Intelligence
Game theory-based algorithms
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Swarm
Intelligence
Based on modeling
natural
systems
(ant
colonies,
flocks of birds)
High adaptability
to changes in the
environment.
Ability to achieve
optimal solutions
without centralized
management.
A global optimal
solution is not
always guaranteed.
Possibility of local
extrema in search
problems
Game theory-
based algorithms
Robots
are
considered
as
players who seek to
maximize
their
benefits.
The
concept of Nash
equilibrium is used.
Efficiency
in
resource allocation.
High formalization
of the mathematical
model
Requirements for a
significant amount
of calculations.
Vulnerability
to
malicious actions
(in case of partial
failure).
All these shortcomings indicate that existing methods often do not take into
account the specific needs of group dynamics of collaborative robots in complex and
dynamic environments, such as flexibility in decision-making, synchronization of
actions and data integration. This emphasizes the need for further research aimed at
creating new or improving existing algorithms that can take into account these
challenges and ensure high efficiency of decentralized management.
Let us develop a classification of the decentralized management model, which is
presented in Figure 2.
Let us conduct a comparative analysis of decentralized control models for a
collaborative robots group, identify their advantages and disadvantages, and present
the results in Table 2.
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Figure 2:
Classification of decentralized control models for a collaborative robots
group
Table 2:
Comparative analysis of decentralized ccontrol models for a collaborative
robots group
Model
Description
Advantages
Disadvantages
Multi-agent
systems model
Each robot acts as
an agent with its
own goals, capable
of
autonomous
decision-making.
Ability to perform
complex tasks by
distributing tasks
between agents.
High autonomy and
adaptability.
High requirements
for creating an
agent
interaction
model.
Difficulty
in
ensuring
consistency
between
agent
actions.
Graph-based
distributed
control model
A group of robots is
represented as a
graph, where nodes
are robots and
edges are their
connections.
Transparency for
modeling
relationships and
interactions.
Using
formal
mathematical
methods.
The difficulty of
maintaining graph
connectivity in the
face of dynamic
changes..
Models based on
potential fields
Each robot moves
in space under the
influence of forces
created by the
Easy to implement
for basic navigation
tasks.
The problem of
getting stuck in
local minima.
Decentralized control models
Multi-agent systems model
Graph-based distributed control model
Models based on potential fields
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environment, other
robots, or target
points.
Low computational
cost.
The inability to
guarantee
global
optimization.
Existing models of a collaborative robots group decentralized control
demonstrate significant advantages, but their shortcomings limit the effectiveness of
application in real conditions. The multi-agent system model provides high autonomy
and adaptability of each robot, but at the same time complicates the creation of an
effective model of interaction between agents. This can lead to problems with the
consistency of actions, especially in the context of performing joint tasks in dynamic
environments. The graph-based distributed control model allows for the formalization
of robot interaction, but its application is complicated by the need to maintain the
connectivity of the graph in conditions of constant changes, such as the failure of
individual robots or a change in the environment. Models based on potential fields are
characterized by simplicity of implementation and low computational costs, but they
have significant limitations, in particular, the tendency to get stuck in local minima and
the inability to achieve global optimization. In general, each of the models has a certain
area of effective application, but none of them is universal for solving the problems of
decentralized control in large groups of robots with a high degree of autonomy. These
limitations indicate the need to improve existing approaches and create new ones that
could take into account the complexity of modern robotic systems, in particular their
ability to operate in a dynamic environment, interact without conflicts and effectively
achieve common goals.
Let us classify a modern decentralized control algorithm, which is presented in
Figure 3.
Decentralized control algorithms
Cooperative Pathfinding
Алгоритми самоорганізації
Navigation by local interaction rules
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Figure 3:
Classification of algorithms for a collaborative robots group
decentralized control
Let us conduct a comparative analysis of algorithms for a collaborative robots
group decentralized contro, identify their advantages and disadvantages, and present
the results in Table 3.
Self-organization algorithms, despite their high adaptability to changing
conditions, have significant limitations that affect their effectiveness in the tasks of a
collaborative robots group decentralized control.
The use of approaches such as clustering in motion or mutual following provides
simplicity in implementing basic tasks of resource allocation or coordination, but does
not guarantee the performance of the system at the group level.
The lack of clear mechanisms for controlling the general behavior can lead to
inefficient use of resources and loss of time for error correction. Navigation based on
local interaction rules, such as Boids algorithms, is convenient for modeling natural
behavior, but their accuracy is insufficient for complex tasks that require a high level
of coordination between robots.
Table 3:
Comparative analysis of algorithms for a collaborative robots group
decentralized control
Algorithm
Description
Advantages
Disadvantages
Cooperative
Pathfinding
Used
to
avoid
collisions between
robots and ensure
the performance of
collective
tasks.
(Algorithm
A*
with
task
distribution
between
robots.
Algorithms D* and
LPA*)
Efficiency
for
complex
navigation tasks.
Providing dynamic
route replanning.
High
computational
costs
in
large
groups.
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Self-organization
algorithms
Used for clustering,
sorting or resource
allocation tasks.
(Clustering
in
Motion.
Follower-Leader.
High adaptability
to conditions.
No
guarantees
regarding
the
performance of the
system as a whole.
Navigation by
local interaction
rules
Algorithms based
on Boids model
movement in a
flock, with the
following rules of
behavior: collision
avoidance, speed
equalization,
attraction
to
neighbors.
Easy to implement.
High flexibility.
Insufficient
accuracy in tasks
with
high
coordination
requirements.
The dependence on local information and simple interaction rules limits the
ability to take into account global goals and the context of the entire system.
Such algorithms are flexible in solving problems in unstable conditions, but their
effectiveness in complex scenarios, where synchronization of actions and consideration
of long-term strategies are required, is significantly reduced. Thus, although self-
organization methods are useful for basic tasks of decentralized control, their
limitations indicate the need for additional approaches that could provide both
adaptability and high accuracy and consistency of the work of a group of robots.
Conclusion
Analysis of existing methods, models and algorithms for a collaborative robots
group decentralized control reveals significant limitations that limit their effectiveness
in complex and dynamic environments. Distributed algorithms demonstrate scalability
and fault tolerance, but their effectiveness is reduced due to limited accuracy and
complexity of coordination in large groups. Swarm intelligence algorithms provide
adaptability to changes, but are prone to local extrema and do not guarantee the
achievement of global optima. Game theory-based methods provide formalization and
efficient resource allocation, but require significant computational resources and may
be vulnerable to partial failures.
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Multi-agent system models allow tasks to be distributed among autonomous
agents, but the complexity of coordinating the actions of agents and high requirements
for their interaction limit their practicality. Graph models are effective for modeling
connections, but require constant maintenance of connectivity in dynamic conditions.
Potential field-based methods are simple to implement, but prone to getting stuck in
local minima, which limits their applicability to complex problems.
Cooperative routing algorithms allow for dynamic replanning of routes, but
require significant computational resources in large systems. Self-organization
algorithms, although they demonstrate adaptability, do not guarantee system
performance at the group level, while methods based on local interaction (e.g. Boids)
have insufficient accuracy for problems with high coordination requirements.
These shortcomings indicate the need to develop new methodologies that can
combine adaptability, efficiency, and consistency of system operation. Modern
approaches should take into account the increasing complexity of tasks in robotics,
integrate elements of artificial intelligence, deep learning, and cyber-physical systems,
while ensuring scalability, energy efficiency, and stability in dynamic conditions.
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ISSN: 3060-4745
IF(Impact Factor)10.41 / 2024
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